utils/gluon/utils/mobilenetv1.py [111:135]:
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                                       kernel_size=1, prefix='classifier_')
            self.flat = gluon.nn.Flatten()

    def _get_channles(self, width, ratio):
        width = (width - int(ratio * width), int(ratio * width))
        width = tuple(c if c != 0 else -1 for c in width)
        return width

    def _concat(self, F, x1, x2):
        if (x1 is not None) and (x2 is not None):
            return F.Concat(x1, x2, dim=1)
        else:
            return x1 if x2 is None else x2

    def hybrid_forward(self, F, x):

        x = self.conv1(x)

        x = (x, None)
        for iy in range(1, 10):
            # assume the max number of blocks is 50 per stage
            for ib in range(0, 50):
                name = 'L%d_B%d' % (iy, ib)
                if hasattr(self, name):
                    x = getattr(self, name)(*x)
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utils/gluon/utils/mobilenetv2.py [158:182]:
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                                       kernel_size=1, prefix='classifier_')
            self.flat = gluon.nn.Flatten()

    def _get_channles(self, width, ratio):
        width = (width - int(ratio * width), int(ratio * width))
        width = tuple(c if c != 0 else -1 for c in width)
        return width

    def _concat(self, F, x1, x2):
        if (x1 is not None) and (x2 is not None):
            return F.Concat(x1, x2, dim=1)
        else:
            return x1 if x2 is None else x2

    def hybrid_forward(self, F, x):

        x = self.conv1(x)

        x = (x, None)
        for iy in range(1, 10):
            # assume the max number of blocks is 50 per stage
            for ib in range(0, 50):
                name = 'L%d_B%d' % (iy, ib)
                if hasattr(self, name):
                    x = getattr(self, name)(*x)
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